{"id":475960,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:42","modified_gmt":"2023-09-05T11:11:42","slug":"backpropagation","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/backpropagation\/","title":{"rendered":"\u53cd\u5411\u4f20\u64ad"},"content":{"rendered":"<p>\u53cd\u5411\u4f20\u64ad\u662f\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc (ANN) \u4e2d\u7528\u4e8e\u8bad\u7ec3\u548c\u4f18\u5316\u76ee\u7684\u7684\u57fa\u672c\u7b97\u6cd5\u3002\u5b83\u5728\u4f7f\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u5e76\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u63d0\u9ad8\u5176\u6027\u80fd\u65b9\u9762\u53d1\u6325\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\u3002\u53cd\u5411\u4f20\u64ad\u7684\u6982\u5ff5\u53ef\u4ee5\u8ffd\u6eaf\u5230\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u7684\u65e9\u671f\uff0c\u5e76\u4ece\u6b64\u6210\u4e3a\u73b0\u4ee3\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u7684\u57fa\u77f3\u3002<\/p>\n<h2>\u53cd\u5411\u4f20\u64ad\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u53cd\u5411\u4f20\u64ad\u7684\u8d77\u6e90\u53ef\u4ee5\u8ffd\u6eaf\u5230 20 \u4e16\u7eaa 60 \u5e74\u4ee3\uff0c\u5f53\u65f6\u7814\u7a76\u4eba\u5458\u5f00\u59cb\u63a2\u7d22\u81ea\u52a8\u8bad\u7ec3\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u7684\u65b9\u6cd5\u3002 1961 \u5e74\uff0cStuart Dreyfus \u5728\u4ed6\u7684\u535a\u58eb\u8bba\u6587\u4e2d\u9996\u6b21\u5c1d\u8bd5\u901a\u8fc7\u7c7b\u4f3c\u4e8e\u53cd\u5411\u4f20\u64ad\u7684\u8fc7\u7a0b\u6765\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u3002\u8bba\u6587\u3002\u7136\u800c\uff0c\u76f4\u5230 20 \u4e16\u7eaa 70 \u5e74\u4ee3\uff0cPaul Werbos \u5728\u4f18\u5316 ANN \u5b66\u4e60\u8fc7\u7a0b\u7684\u5de5\u4f5c\u4e2d\u624d\u9996\u6b21\u4f7f\u7528\u201c\u53cd\u5411\u4f20\u64ad\u201d\u4e00\u8bcd\u3002\u53cd\u5411\u4f20\u64ad\u5728 20 \u4e16\u7eaa 80 \u5e74\u4ee3\u83b7\u5f97\u4e86\u6781\u5927\u7684\u5173\u6ce8\uff0c\u5f53\u65f6 Rumelhart\u3001Hinton \u548c Williams \u63a8\u51fa\u4e86\u8be5\u7b97\u6cd5\u7684\u66f4\u9ad8\u6548\u7248\u672c\uff0c\u8fd9\u518d\u6b21\u6fc0\u53d1\u4e86\u4eba\u4eec\u5bf9\u795e\u7ecf\u7f51\u7edc\u7684\u5174\u8da3\u3002<\/p>\n<h2>\u6709\u5173\u53cd\u5411\u4f20\u64ad\u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898<\/h2>\n<p>\u53cd\u5411\u4f20\u64ad\u662f\u4e00\u79cd\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\uff0c\u4e3b\u8981\u7528\u4e8e\u8bad\u7ec3\u591a\u5c42\u795e\u7ecf\u7f51\u7edc\u3002\u5b83\u6d89\u53ca\u901a\u8fc7\u7f51\u7edc\u5411\u524d\u9988\u9001\u8f93\u5165\u6570\u636e\uff0c\u8ba1\u7b97\u9884\u6d4b\u8f93\u51fa\u548c\u5b9e\u9645\u8f93\u51fa\u4e4b\u95f4\u7684\u8bef\u5dee\u6216\u635f\u5931\uff0c\u7136\u540e\u901a\u8fc7\u5404\u5c42\u5411\u540e\u4f20\u64ad\u8be5\u8bef\u5dee\u4ee5\u66f4\u65b0\u7f51\u7edc\u6743\u91cd\u7684\u8fed\u4ee3\u8fc7\u7a0b\u3002\u8fd9\u4e2a\u8fed\u4ee3\u8fc7\u7a0b\u4e00\u76f4\u6301\u7eed\u5230\u7f51\u7edc\u6536\u655b\u5230\u8bef\u5dee\u6700\u5c0f\u5316\u7684\u72b6\u6001\uff0c\u5e76\u4e14\u7f51\u7edc\u53ef\u4ee5\u51c6\u786e\u5730\u9884\u6d4b\u65b0\u8f93\u5165\u6570\u636e\u7684\u671f\u671b\u8f93\u51fa\u3002<\/p>\n<h2>\u53cd\u5411\u4f20\u64ad\u7684\u5185\u90e8\u7ed3\u6784\uff1a\u53cd\u5411\u4f20\u64ad\u5982\u4f55\u5de5\u4f5c<\/h2>\n<p>\u53cd\u5411\u4f20\u64ad\u7684\u5185\u90e8\u7ed3\u6784\u53ef\u4ee5\u5206\u4e3a\u51e0\u4e2a\u5173\u952e\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li>\n<p>\u524d\u5411\u4f20\u9012\uff1a\u5728\u524d\u5411\u4f20\u9012\u671f\u95f4\uff0c\u8f93\u5165\u6570\u636e\u9010\u5c42\u9988\u9001\u5230\u795e\u7ecf\u7f51\u7edc\uff0c\u5728\u6bcf\u4e00\u5c42\u5e94\u7528\u4e00\u7ec4\u52a0\u6743\u8fde\u63a5\u548c\u6fc0\u6d3b\u51fd\u6570\u3002\u5c06\u7f51\u7edc\u7684\u8f93\u51fa\u4e0e\u5730\u9762\u5b9e\u51b5\u8fdb\u884c\u6bd4\u8f83\u4ee5\u8ba1\u7b97\u521d\u59cb\u8bef\u5dee\u3002<\/p>\n<\/li>\n<li>\n<p>\u5411\u540e\u4f20\u9012\uff1a\u5728\u5411\u540e\u4f20\u9012\u4e2d\uff0c\u8bef\u5dee\u4ece\u8f93\u51fa\u5c42\u5411\u540e\u4f20\u64ad\u5230\u8f93\u5165\u5c42\u3002\u8fd9\u662f\u901a\u8fc7\u5e94\u7528\u5fae\u79ef\u5206\u7684\u94fe\u5f0f\u6cd5\u5219\u6765\u8ba1\u7b97\u7f51\u7edc\u4e2d\u6bcf\u4e2a\u6743\u91cd\u7684\u8bef\u5dee\u68af\u5ea6\u6765\u5b9e\u73b0\u7684\u3002<\/p>\n<\/li>\n<li>\n<p>\u6743\u91cd\u66f4\u65b0\uff1a\u83b7\u5f97\u68af\u5ea6\u540e\uff0c\u4f7f\u7528\u4f18\u5316\u7b97\u6cd5\u66f4\u65b0\u7f51\u7edc\u7684\u6743\u91cd\uff0c\u4f8b\u5982\u968f\u673a\u68af\u5ea6\u4e0b\u964d\uff08SGD\uff09\u6216\u5176\u53d8\u4f53\u4e4b\u4e00\u3002\u8fd9\u4e9b\u66f4\u65b0\u65e8\u5728\u6700\u5927\u9650\u5ea6\u5730\u51cf\u5c11\u8bef\u5dee\uff0c\u8c03\u6574\u7f51\u7edc\u53c2\u6570\u4ee5\u505a\u51fa\u66f4\u597d\u7684\u9884\u6d4b\u3002<\/p>\n<\/li>\n<li>\n<p>\u8fed\u4ee3\u8fc7\u7a0b\uff1a\u524d\u5411\u548c\u540e\u5411\u4f20\u9012\u8fed\u4ee3\u91cd\u590d\u4e00\u5b9a\u6570\u91cf\u7684 epoch \u6216\u76f4\u5230\u6536\u655b\uff0c\u4ece\u800c\u9010\u6e10\u63d0\u9ad8\u7f51\u7edc\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u53cd\u5411\u4f20\u64ad\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<p>\u53cd\u5411\u4f20\u64ad\u63d0\u4f9b\u4e86\u51e0\u4e2a\u5173\u952e\u529f\u80fd\uff0c\u4f7f\u5176\u6210\u4e3a\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u7684\u5f3a\u5927\u7b97\u6cd5\uff1a<\/p>\n<ul>\n<li>\n<p><strong>\u591a\u529f\u80fd\u6027<\/strong>\uff1a\u53cd\u5411\u4f20\u64ad\u53ef\u7528\u4e8e\u591a\u79cd\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u5305\u62ec\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc (RNN) \u548c\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN)\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6548\u7387<\/strong>\uff1a\u5c3d\u7ba1\u8ba1\u7b97\u91cf\u5f88\u5927\uff0c\u4f46\u53cd\u5411\u4f20\u64ad\u591a\u5e74\u6765\u4e00\u76f4\u5728\u4f18\u5316\uff0c\u4f7f\u5176\u80fd\u591f\u6709\u6548\u5730\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u548c\u590d\u6742\u7f51\u7edc\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u53cd\u5411\u4f20\u64ad\u7684\u5e76\u884c\u7279\u6027\u4f7f\u5176\u5177\u6709\u53ef\u6269\u5c55\u6027\uff0c\u4f7f\u5176\u80fd\u591f\u5229\u7528\u73b0\u4ee3\u786c\u4ef6\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u8d44\u6e90\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u975e\u7ebf\u6027<\/strong>\uff1a\u53cd\u5411\u4f20\u64ad\u5904\u7406\u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570\u7684\u80fd\u529b\u5141\u8bb8\u795e\u7ecf\u7f51\u7edc\u5bf9\u6570\u636e\u5185\u7684\u590d\u6742\u5173\u7cfb\u8fdb\u884c\u5efa\u6a21\u3002<\/p>\n<\/li>\n<\/ul>\n<h2>\u53cd\u5411\u4f20\u64ad\u7684\u7c7b\u578b<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6807\u51c6\u53cd\u5411\u4f20\u64ad<\/td>\n<td>\u4f7f\u7528\u76f8\u5bf9\u4e8e\u6bcf\u4e2a\u6743\u91cd\u7684\u8bef\u5dee\u7684\u5b8c\u6574\u68af\u5ea6\u6765\u66f4\u65b0\u6743\u91cd\u7684\u539f\u59cb\u7b97\u6cd5\u3002\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\u6765\u8bf4\uff0c\u8ba1\u7b97\u6210\u672c\u53ef\u80fd\u5f88\u9ad8\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u968f\u673a\u53cd\u5411\u4f20\u64ad<\/td>\n<td>\u6807\u51c6\u53cd\u5411\u4f20\u64ad\u7684\u4f18\u5316\uff0c\u5728\u6bcf\u4e2a\u5355\u72ec\u7684\u6570\u636e\u70b9\u4e4b\u540e\u66f4\u65b0\u6743\u91cd\uff0c\u51cf\u5c11\u8ba1\u7b97\u8981\u6c42\uff0c\u4f46\u5728\u6743\u91cd\u66f4\u65b0\u4e2d\u5f15\u5165\u66f4\u591a\u968f\u673a\u6027\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u5c0f\u6279\u91cf\u53cd\u5411\u4f20\u64ad<\/td>\n<td>\u6807\u51c6\u53cd\u5411\u4f20\u64ad\u548c\u968f\u673a\u53cd\u5411\u4f20\u64ad\u4e4b\u95f4\u7684\u6298\u8877\uff0c\u66f4\u65b0\u6279\u91cf\u6570\u636e\u70b9\u7684\u6743\u91cd\u3002\u5b83\u5728\u8ba1\u7b97\u6548\u7387\u548c\u6743\u91cd\u66f4\u65b0\u7a33\u5b9a\u6027\u4e4b\u95f4\u53d6\u5f97\u4e86\u5e73\u8861\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u6279\u91cf\u53cd\u5411\u4f20\u64ad<\/td>\n<td>\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u5728\u66f4\u65b0\u6743\u91cd\u4e4b\u524d\u8ba1\u7b97\u6574\u4e2a\u6570\u636e\u96c6\u7684\u68af\u5ea6\u3002\u5b83\u4e3b\u8981\u7528\u4e8e\u5e76\u884c\u8ba1\u7b97\u73af\u5883\u4e2d\uff0c\u4ee5\u6709\u6548\u5229\u7528 GPU \u6216 TPU\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4f7f\u7528\u53cd\u5411\u4f20\u64ad\u7684\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<p><strong>\u4f7f\u7528\u53cd\u5411\u4f20\u64ad<\/strong><\/p>\n<ul>\n<li>\u56fe\u50cf\u8bc6\u522b\uff1a\u53cd\u5411\u4f20\u64ad\u5e7f\u6cdb\u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u4efb\u52a1\uff0c\u5176\u4e2d\u8bad\u7ec3\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN) \u6765\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u5bf9\u8c61\u548c\u6a21\u5f0f\u3002<\/li>\n<li>\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff1a\u53cd\u5411\u4f20\u64ad\u53ef\u7528\u4e8e\u8bad\u7ec3\u9012\u5f52\u795e\u7ecf\u7f51\u7edc (RNN)\uff0c\u4ee5\u8fdb\u884c\u8bed\u8a00\u5efa\u6a21\u3001\u673a\u5668\u7ffb\u8bd1\u548c\u60c5\u611f\u5206\u6790\u3002<\/li>\n<li>\u8d22\u52a1\u9884\u6d4b\uff1a\u53cd\u5411\u4f20\u64ad\u53ef\u7528\u4e8e\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u3001\u5e02\u573a\u8d8b\u52bf\u548c\u5176\u4ed6\u8d22\u52a1\u6307\u6807\u3002<\/li>\n<\/ul>\n<p><strong>\u6311\u6218\u4e0e\u89e3\u51b3\u65b9\u6848<\/strong><\/p>\n<ul>\n<li><strong>\u68af\u5ea6\u6d88\u5931\u95ee\u9898<\/strong>\uff1a\u5728\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u4e2d\uff0c\u53cd\u5411\u4f20\u64ad\u671f\u95f4\u68af\u5ea6\u53ef\u80fd\u53d8\u5f97\u975e\u5e38\u5c0f\uff0c\u5bfc\u81f4\u6536\u655b\u7f13\u6162\u751a\u81f3\u505c\u6b62\u5b66\u4e60\u8fc7\u7a0b\u3002\u89e3\u51b3\u65b9\u6848\u5305\u62ec\u4f7f\u7528 ReLU \u7b49\u6fc0\u6d3b\u51fd\u6570\u548c\u6279\u91cf\u5f52\u4e00\u5316\u7b49\u6280\u672f\u3002<\/li>\n<li><strong>\u8fc7\u62df\u5408<\/strong>\uff1a\u53cd\u5411\u4f20\u64ad\u53ef\u80fd\u4f1a\u5bfc\u81f4\u8fc7\u5ea6\u62df\u5408\uff0c\u5373\u7f51\u7edc\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u8868\u73b0\u826f\u597d\uff0c\u4f46\u5728\u672a\u89c1\u8fc7\u7684\u6570\u636e\u4e0a\u8868\u73b0\u4e0d\u4f73\u3002 L1 \u548c L2 \u6b63\u5219\u5316\u7b49\u6b63\u5219\u5316\u6280\u672f\u53ef\u4ee5\u5e2e\u52a9\u51cf\u8f7b\u8fc7\u5ea6\u62df\u5408\u3002<\/li>\n<li><strong>\u8ba1\u7b97\u5f3a\u5ea6<\/strong>\uff1a\u8bad\u7ec3\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u53ef\u80fd\u9700\u8981\u5927\u91cf\u8ba1\u7b97\uff0c\u5c24\u5176\u662f\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\u3002\u4f7f\u7528GPU\u6216TPU\u8fdb\u884c\u52a0\u901f\u5e76\u4f18\u5316\u7f51\u7edc\u67b6\u6784\u53ef\u4ee5\u7f13\u89e3\u8fd9\u4e2a\u95ee\u9898\u3002<\/li>\n<\/ul>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u5176\u4ed6\u4e0e\u540c\u7c7b\u4ea7\u54c1\u7684\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u53cd\u5411\u4f20\u64ad<\/th>\n<th>\u68af\u5ea6\u4e0b\u964d<\/th>\n<th>\u968f\u673a\u68af\u5ea6\u4e0b\u964d<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u7c7b\u578b<\/td>\n<td>\u7b97\u6cd5<\/td>\n<td>\u4f18\u5316\u7b97\u6cd5<\/td>\n<td>\u4f18\u5316\u7b97\u6cd5<\/td>\n<\/tr>\n<tr>\n<td>\u76ee\u7684<\/td>\n<td>\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3<\/td>\n<td>\u529f\u80fd\u4f18\u5316<\/td>\n<td>\u529f\u80fd\u4f18\u5316<\/td>\n<\/tr>\n<tr>\n<td>\u66f4\u65b0\u9891\u7387<\/td>\n<td>\u6bcf\u6279\u540e<\/td>\n<td>\u6bcf\u4e2a\u6570\u636e\u70b9\u4e4b\u540e<\/td>\n<td>\u6bcf\u4e2a\u6570\u636e\u70b9\u4e4b\u540e<\/td>\n<\/tr>\n<tr>\n<td>\u8ba1\u7b97\u6548\u7387<\/td>\n<td>\u7f13\u548c<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u4e2d\u5230\u9ad8<\/td>\n<\/tr>\n<tr>\n<td>\u6297\u566a\u58f0\u9c81\u68d2\u6027<\/td>\n<td>\u7f13\u548c<\/td>\n<td>\u4f4e\u7684<\/td>\n<td>\u4e2d\u5ea6\u81f3\u4f4e\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u53cd\u5411\u4f20\u64ad\u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f<\/h2>\n<p>\u53cd\u5411\u4f20\u64ad\u7684\u672a\u6765\u4e0e\u786c\u4ef6\u548c\u7b97\u6cd5\u7684\u8fdb\u6b65\u5bc6\u5207\u76f8\u5173\u3002\u968f\u7740\u8ba1\u7b97\u80fd\u529b\u7684\u4e0d\u65ad\u589e\u5f3a\uff0c\u8bad\u7ec3\u66f4\u5927\u3001\u66f4\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u5c06\u53d8\u5f97\u66f4\u52a0\u53ef\u884c\u3002\u6b64\u5916\uff0c\u7814\u7a76\u4eba\u5458\u6b63\u5728\u79ef\u6781\u63a2\u7d22\u4f20\u7edf\u53cd\u5411\u4f20\u64ad\u7684\u66ff\u4ee3\u65b9\u6848\uff0c\u4f8b\u5982\u8fdb\u5316\u7b97\u6cd5\u548c\u53d7\u751f\u7269\u5b66\u542f\u53d1\u7684\u5b66\u4e60\u65b9\u6cd5\u3002<\/p>\n<p>\u6b64\u5916\uff0c\u65b0\u9896\u7684\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\uff0c\u4f8b\u5982\u53d8\u538b\u5668\u548c\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u5df2\u7ecf\u53d7\u5230\u6b22\u8fce\uff0c\u5e76\u53ef\u80fd\u5f71\u54cd\u53cd\u5411\u4f20\u64ad\u6280\u672f\u7684\u53d1\u5c55\u3002\u53cd\u5411\u4f20\u64ad\u4e0e\u8fd9\u4e9b\u65b0\u67b6\u6784\u7684\u7ed3\u5408\u53ef\u80fd\u4f1a\u5728\u5404\u4e2a\u9886\u57df\u4ea7\u751f\u66f4\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u7ed3\u679c\u3002<\/p>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5982\u4f55\u5c06\u4ee3\u7406\u670d\u52a1\u5668\u4e0e\u53cd\u5411\u4f20\u64ad\u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5728\u652f\u6301\u53cd\u5411\u4f20\u64ad\u4efb\u52a1\u65b9\u9762\u53ef\u4ee5\u53d1\u6325\u91cd\u8981\u4f5c\u7528\uff0c\u7279\u522b\u662f\u5728\u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u80cc\u666f\u4e0b\u3002\u7531\u4e8e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u9700\u8981\u5927\u91cf\u6570\u636e\u548c\u8ba1\u7b97\u80fd\u529b\uff0c\u7814\u7a76\u4eba\u5458\u7ecf\u5e38\u5229\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6765\u4fc3\u8fdb\u66f4\u5feb\u7684\u6570\u636e\u68c0\u7d22\u3001\u7f13\u5b58\u8d44\u6e90\u548c\u4f18\u5316\u7f51\u7edc\u6d41\u91cf\u3002\u901a\u8fc7\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\uff0c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u589e\u5f3a\u6570\u636e\u8bbf\u95ee\u5e76\u6700\u5927\u9650\u5ea6\u5730\u51cf\u5c11\u5ef6\u8fdf\uff0c\u4ece\u800c\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u8bad\u7ec3\u548c\u5b9e\u9a8c\u795e\u7ecf\u7f51\u7edc\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/05\/understanding-backpropagation-in-neural-networks\/\" target=\"_new\" rel=\"noopener nofollow\">\u4e86\u89e3\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u53cd\u5411\u4f20\u64ad<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/backpropagation-improving-neural-networks-23b1b3ea4d28\" target=\"_new\" rel=\"noopener nofollow\">\u53cd\u5411\u4f20\u64ad\uff1a\u6539\u8fdb\u795e\u7ecf\u7f51\u7edc<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/gentle-introduction-backpropagation-time\/\" target=\"_new\" rel=\"noopener nofollow\">\u53cd\u5411\u4f20\u64ad\u7684\u7b80\u5355\u4ecb\u7ecd<\/a><\/li>\n<li><a href=\"http:\/\/neuralnetworksanddeeplearning.com\/chap2.html\" target=\"_new\" rel=\"noopener nofollow\">\u795e\u7ecf\u7f51\u7edc\u548c\u6df1\u5ea6\u5b66\u4e60<\/a><\/li>\n<\/ul>","protected":false},"featured_media":475755,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475960","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Backpropagation: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Backpropagation?","answer":"<p>Backpropagation is a fundamental algorithm used in artificial neural networks (ANNs) for training and optimization. It enables ANNs to learn from data and improve their performance over time.<\/p>"},{"question":"How did Backpropagation originate?","answer":"<p>The concept of backpropagation dates back to the 1960s, with early attempts made by Stuart Dreyfus in his Ph.D. thesis. The term \"backpropagation\" was first used by Paul Werbos in the 1970s. It gained significant attention in the 1980s when Rumelhart, Hinton, and Williams introduced a more efficient version of the algorithm.<\/p>"},{"question":"How does Backpropagation work?","answer":"<p>Backpropagation involves a forward pass, where input data is fed through the network, followed by a backward pass, where the error is propagated backward from the output to the input layer. This iterative process updates the network's weights until the error is minimized.<\/p>"},{"question":"What are the key features of Backpropagation?","answer":"<p>Backpropagation is versatile, efficient, scalable, and capable of handling non-linear activation functions. These features make it a powerful algorithm for training neural networks.<\/p>"},{"question":"What types of Backpropagation exist?","answer":"<p>There are several types of backpropagation, including Standard Backpropagation, Stochastic Backpropagation, Mini-batch Backpropagation, and Batch Backpropagation. Each has its advantages and trade-offs.<\/p>"},{"question":"How can Backpropagation be used?","answer":"<p>Backpropagation finds application in various domains, such as image recognition, natural language processing, and financial forecasting.<\/p>"},{"question":"What challenges are associated with Backpropagation, and how can they be solved?","answer":"<p>Backpropagation faces challenges like the vanishing gradient problem and overfitting. Solutions include using activation functions like ReLU, regularization techniques, and optimizing the network architecture.<\/p>"},{"question":"How does Backpropagation compare to Gradient Descent and Stochastic Gradient Descent?","answer":"<p>Backpropagation is an algorithm used in neural network training, while Gradient Descent and Stochastic Gradient Descent are optimization algorithms for function optimization. They differ in update frequency and computational efficiency.<\/p>"},{"question":"What does the future hold for Backpropagation?","answer":"<p>The future of backpropagation lies in advancements in hardware and algorithms, as well as exploring alternatives and combining it with novel neural network architectures.<\/p>"},{"question":"How are Proxy Servers associated with Backpropagation?","answer":"<p>Proxy servers support backpropagation tasks, particularly in large-scale distributed training, by enhancing data access and minimizing latency, leading to more efficient training with neural networks.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/475960","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/475960\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/475755"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=475960"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}